API World + AI DevWorld

With the active launches of new government and commercial Earth Observation satellites it has become possible to observe the food growing process from satellites daily at a global scale for the commodity crops like corn, wheat and sugarcane. Based on the satellite data we can identify and share agriculture-specific insights like: presence of farming activity, presence of irrigation systems, crop classification and productivity assessment.
A pipeline starts with a set of images specifically designed for daily monitoring the growth of commodity crops: corn, soybean, rice and wheat. To process this data we use our processing and delivery system with AI/ML (boosting) used for understanding vegetation patterns and AI for scaling the models on other climate zones.
The main challenges of this approach is availability of regional dataset to understand the context and working with massive datasets like over 10PB of just Open Source Earth Observation data.
It the presentation I will provide an overview of existing data sources for analyzing food production patterns at global scale, overview of implementation techniques to process data cubes covering the whole World with various remote sensing data source and a case study of how to iterate with data processing and delivery infrastructure for satellite imagery.